Alopecia areata (AA) is a disfiguring human autoimmune skin disease with a complex genetic basis that targets hair follicles in the actively growing anagen phase and is often associated with other systemic autoimmune diseases. Psychogenic trauma due to hair loss, particularly for teen-aged girls, can be so serious as to lead to suicide. AA can affect up to 2% of the general population at some time during their lives (6,000,000 US citizens). C3H/HeJ mice spontaneously develop AA and, like in humans, this is a very complex polygenic disease with susceptibility loci in common with both human AA patients and the rat AA model. Our previous hypothesis was that AA was due to the interaction of immune-regulatory genes affecting pathways that can be identified by systematic analysis of gene expression levels within each quantitative trait locus (QTL) interval previously identified. While this idea remains viable, we have now identified genes within these QTLs which go beyond immune regulatory genes towards explaining many of the key issues in the pathogenesis of this extremely complicated disease. Gene array data from a cross sectional AA study (completed, including mice with spontaneous disease), when combined with quantitative real time RT PCR (QPCR) for all genes within each of the 4 AA quantitative trait loci (QTLs) and haplotype mapping data based on total genomic sequencing (Sanger draft sequences), will be used to identify numerous candidate genes involved in the pathogenesis of AA that will allow for prioritization of molecular pathways for future intervention trials. To validate this work we will use a prototype mouse AA-specific QPCR 384 gene array (completed) to provide expression based phenotyping capabilities, adding Expression-based Quantitative Trait Locus (EQTL) analytical methods to analyze new genetic crosses. By evaluating mice that develop AA in the separate aging of Collaborative Cross mice (nearly 200 carefully genotyped novel mouse strains that provide expanded genetic diversity), we will identify new QTLs and refine the known QTLs. In so doing, we will reduce the mouse genetic intervals, find and test candidate genes, identify new loci, and validate our new molecular tools that will eventually add value to drug and diagnostic screening approaches. Discoveries using this mouse model continue to contribute to a better understanding and treatment options for human AA, especially since recent human genetic linkage studies found corresponding genetic intervals to those in our mouse model.